A New Effective and Robust Index Using Spectral Curve Shapes and Local Reflectance Peaks: Detecting Simple Coated Steel-Tiled Houses in Urban Areas

Simple steel-tiled houses (SSHs), as lightweight and economical building materials, have been widely utilized in the process of urban development. However, SSH inevitably causes environmental impacts, such as local heat islands. Acquiring spatial information on SSH is crucial for urban planning and understanding the anthropogenic influence on the environment. However, this prevalent artificial structure in urban areas has not received sufficient attention. SSH are often coated in various colors, making them particularly challenging to detect within complex urban backgrounds. Coated steel tile (CST) is an important feature of SSH. Based on Sentinel-2 imagery, this study constructs two intermediate variables (CSI and $\rho $ max) to develop an advanced spectral index, termed CST index (CSTI), for detecting SSH in urban areas. The results demonstrate that CSTI outperforms the existing indices across all study areas, with an overall accuracy (OA) exceeding 0.95. Compared with supervised classification methods, the CSTI-based method achieves satisfactory mapping accuracy without the need for training samples. Moreover, CSTI is robust across different seasonal images and other sensors. Finally, in six Chinese cities (Harbin, Shenyang, Qinhuangdao, Zhengzhou, Jinan, and Linyi), SSH shows an aggregation phenomenon in urban centers, with an expansion trend in developing areas over the past decade. In conclusion, this study introduces an effective and robust method for SSH detection, expected to provide strong support for urban land management and ecological assessment.